similar to: natural splines

I'm using R 1.7.0 on linux. With this version of R the package modreg is automatically loaded at start of session. However attempting to use predict.smooth.spline() produces Error: couldn't find function predict.smooth.spline. The function smooth.spline() is OK. What am I missing? ====================================== I.White ICAPB, University of Edinburgh Ashworth Laboratories, West

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Can anyone explain why with latest version of R (2.0.1) on FC3, installed from R-2.0.1-0.fdr.2.fc3.i386.rpm, update.packages() produces the message /usr/lib/R/bin/Rcmd exec: INSTALL: not found. Indeed /usr/lib/R/bin seems to lack various shell scripts (INSTALL, REMOVE, etc). ====================================== I.White University of Edinburgh Ashworth Laboratories, West Mains Road Edinburgh

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### # The last line of following code produces a segmentation fault: x <- 1:10 f <- gl(5,2)

Hi, You should look at Pinheiro and Bates (2000) Mixed-effects models in S and S-Plus. It describes how to format the correlation matrix to pass to functions lme and gls. Basically, the correlation matrix has to be one of the corStruct classes, probably corSymm for your example. So in the call to lme (or gls if you really have no random effects), use something like:

Could anyone help with a linear mixed model fitting problem ? The model is : Y= Xp + Zu + e where X, Z are known design matrix, p is fixed effect factor, u is random effect, u~ (0, G) , e~(0,R) The main problem is , I want to fix the covariance matrix G to be a constant times a known covariance matrix A, G = c*A (c is positive constant, A is a predefined matrix with values manually set by

Hello everyone,          Dr. wood told me that I can adapting his example to force cubic spline to pass through certain point.          I still have no idea how to achieve this. Suppose we want to force the cubic spline to pass (1,1), how can I achieve this by adapting the following code? # Penalized example: monotonic penalized regression spline ..... # Generate data from a monotonic truth.

Hi, I'm using natural cubic splines from splines::ns() in survival regression (regressing inter-arrival times of patients to a queue on queue size). The queue size fluctuates between 3600 and 3900. I would like to be able to run predict.survreg() for sizes <3600 and >3900 by assuming that the rate for <3600 is the same as for 3600 and that for >4000 it's the same as for

Goodmorning, This is a documentation related question about the B-spline function in R. In the help file it is stated that: "df degrees of freedom; one can specify df rather than knots; bs() then chooses df-degree-1 knots at suitable quantiles of x (which will ignore missing values)." So if one were to specify a spline with 6 degrees of freedom (and no intercept) then a basis

Hi - How can I 'manually' reproduce the results in 'pred1' below? My attempt is pred_manual, but is not correct. Any help is much appreciated. library(splines) set.seed(12345) y <- rgamma(1000, shape =0.5) age <- rnorm(1000, 45, 10) glm1 <- glm(y ~ ns(age, 4), family=Gamma(link=log)) dd <- data.frame(age = 16:80) mm <- model.matrix( ~ ns(dd$age, 4)) pred1 <-

Hi, all I want to choose same knots in GAM for 10 different studies so that they has the same basis function. Even though I choose same knots and same dimensions of basis smoothing, the basis representations are still not same. My command is as follows: data.gam<-gam(y~s(age,bs='cr',k=10)+male,family=binomial,knots=list(age=seq(45,64,length=10))) What is my mistake for choice of

I am seeking for a method to calculate, given a fitted lme model and some data for a subject, the random effects predictors for this subject. I can only find predictors for the subjects used in creating the fit. Of course I could just add the subject and redo the fit. But I want to avoid just this refitting. Thanks for help wbk

Dear R-helpers, I need to fit natural cubic spline with specified number of knots. I expected 'splines' package will be helpful, but I am confused by its help. Is more detailed documentation available for it or could you recommend another R function? Best regards Ondrej Mikula

After a one-way anova, se.contrast computes the standard error of a contrast, but not the value of the contrast itself. Wouldn't this be useful? Am I missing something? ====================================== I.White ICAPB, University of Edinburgh Ashworth Laboratories, West Mains Road Edinburgh EH9 3JT Fax: 0131 650 6564 Tel: 0131 650 5490 E-mail: iwhite at staffmail.ed.ac.uk

Dear R-users, One of the main reasons I moved from GAUSS to R (as an econometrician) was because of the existence of the library LOCFIT for local polynomial regression. While doing some checking between my former `GAUSS code' and my new `R code', I came to realize LOCFIT is not quite doing what I want. I wrote the following example script:

Hello everyone, I hope this is a fair enough question, but I don’t have access to a copy of Bates and Pinheiro. It is probably quite obvious but the answer might be of general interest. If I fit a fixed effect with an added quadratic term and then do it as an orthogonal polynomial using maximum likelihood I get the expected result- they have the same logLik.

Hello, I am using R and the smooth.Pspline function in the pspline package to smooth some data by using natural cubic splines. After fitting a sufficiently smooth spline using the following call: (ps=smooth.Pspline(x,y,norder=2,spar=0.8,method=1) [the values of x are age in years from 1 to 100] I tried to check that R in fact had fitted a natural cubic spline by checking that the resulting

Dear all, I have two variables, y and x. It seems that the relationship between them is Piecewise Linear Functions. The cutpoint is 20. That is, when x<20, there is a linear relationship between y and x; while x>=20, there is another different linear relationship between them. How can i specify their relationships in R correctly? # glm(y~I(x<20)+I(x>=20),family = binomial, data =

Hello, I am trying to estimate a multivariate regression of Y on X with regression splines. Y is (nx1), and X is (nxd), with d>1. I assume the data is generated by some unknown regression function f(X), as in Y = f(X) + u, where u is some well-behaved regression error. I want to estimate f(X) via regression splines (tensor product splines). Then, I want to get the predicted values for some new

Hi, Does anyone know how to fit a GAM where one or more smooth terms are constrained to be monotonic, in the presence of "by" variables or other terms? I looked at the example in ?pcls but so far have not been able to adapt it to the case where there is more than one predictor. For example, require(mgcv) set.seed(0) n<-100 # Generate data from a monotonic truth.